In this paper, a new command filter-based adaptive NN control strategy is developed to address the prescribed tracking performance issue for a class of nonstrict-feedback nonlinear systems. Compared with the existing performance functions, a new performance function, the fixed-time performance function, which does not depend on the accurate initial value of the error signal and has the ability of fixed-time convergence, is proposed for the first time. A radial basis function neural network is introduced to identify unknown nonlinear functions, and the characteristic of Gaussian basis functions is utilized to overcome the difficulties of the nonstrict-feedback structure. Moreover, in contrast to the traditional Backstepping technique, a command filter-based adaptive control algorithm is constructed, which solves the “explosion of complexity” problem and relaxes the assumption on the reference signal. Additionally, it is guaranteed that the tracking error falls within a prescribed small neighborhood by the designed performance functions in fixed time, and the closed-loop system is semi-globally uniformly ultimately bounded (SGUUB). The effectiveness of the proposed control scheme is verified by numerical simulation. Full article

High-altitude platform (HAP) drones and satellites collaborate to form a network that provides edge computing services to terrestrial internet of things (IoT) devices, which is considered a promising method. In this network, IoT devices’ tasks can be split into multiple parts and processed by servers at non-terrestrial nodes in different locations, thereby reducing task processing delays. However, splitting tasks and allocating communication and computing resources are important challenges. In this paper, we investigate the task offloading and resource allocation problem in multi-HAP drones and multi-satellite collaborative networks. In particular, we formulate a task splitting and communication and computing resource optimization problem to minimize the total delay of all IoT devices’ tasks. To solve this problem, we first transform and decompose the original problem into two subproblems. We design a task splitting optimization algorithm based on deep reinforcement learning, which can achieve online task offloading decision-making. This algorithm structurally designs the actor network to ensure that output actions are always valid. Furthermore, we utilize convex optimization methods to optimize the resource allocation subproblem. The simulation results show that our algorithm can effectively converge and significantly reduce the total task processing delay when compared with other baseline algorithms. Full article

Driven by technological advances in various fields (AI, 5G, VR, IoT, etc.) together with the emergence of digital twins technologies (HDT, HAL, BIM, etc.), the Metaverse has attracted growing attention from scientific and industrial communities. This interest is due to its potential impact on people lives in different sectors such as education or medicine. Specific solutions can also increase inclusiveness of people with disabilities that are an impediment to a fulfilled life. However, security and privacy concerns remain the main obstacles to its development. Particularly, the data involved in the Metaverse can be comprehensive with enough granularity to build a highly detailed digital copy of the real world, including a Human Digital Twin of a person. Existing security countermeasures are largely ineffective and lack adaptability to the specific needs of Metaverse applications. Furthermore, the virtual worlds in a large-scale Metaverse can be highly varied in terms of hardware implementation, communication interfaces, and software, which poses huge interoperability difficulties. This paper aims to analyse the risks and opportunities associated with adopting digital replicas of humans (HDTs) within the Metaverse and the challenges related to managing digital identities in this context. By examining the current technological landscape, we identify several open technological challenges that currently limit the adoption of HDTs and the Metaverse. Additionally, this paper explores a range of promising technologies and methodologies to assess their suitability within the Metaverse context. Finally, two example scenarios are presented in the Medical and Education fields. Full article

This paper deals with the design of a complete state control for input unsigned, rank deficient matrix parameters of a linear system with system dynamics defined by ostensible structures of Metzler matrices. The proposed solution is based on the principle of diagonal stabilization of positive systems and uses a stabilizing additional component over the decomposition of the Metzler matrix in solving the incomplete internal positivity of such linear system structures. The novelty of the proposed approach is the unified representation of the parametric constraints of the Metzler matrix and the structurally constrained system inputs using linear matrix inequalities, which guarantees that the closed-loop system will be asymptotically stable. Despite the complexity of the constraint conditions on this class of linear continuous systems, the design conditions are formulated using sharp linear matrix inequalities only. A detailed design process is presented using a system-linearized mathematical model to verify the superiority and practicality of the proposed method. Full article

In this work, we present an innovative calibration technique leveraging differentiable programming to enhance energy resolution and reduce the energy scale systematic uncertainty in X-ray spectroscopic experiments. This approach is demonstrated using synthetic data and is applicable in general to various spectroscopic measurements. This method extends the scope of differentiable programming for calibration, employing Kernel Density Estimation (KDE) to achieve a target Probability Density Function (PDF) for a fully differentiable model of the calibration. To assess the effectiveness of the calibration, we conduct a toy simulation replicating the entire detector response chain and compare it with a standard calibration. This ensures a robust and reliable calibration methodology, holding promise for improving energy resolution and providing a more versatile and efficient approach without the need for extensive fine-tuning. Full article

Seafood mislabeling threatens customer rights and causes economic loss worldwide. The information on seafood misrepresentation in Thailand is still lacking, and the investigation and monitoring program must be well established. This study investigated the mislabeling status of imported seafood in Thailand using the DNA barcoding technique. A total of 45 imported seafood products from five distributors were included. Scientific, common, local, and market names of seafood samples were obtained from FAO and Fishbase databases. DNA was extracted, and PCR was performed using a universal primer targeting the COI gene. Species of each sample were identified with over 98% similarity based on COI sequence analysis. DNA sequence revealed 11 mislabeled samples. Among substituted species, Pangasianodon hypophthalmus and Thunnus maccoyii were found to be endangered species according to IUCN status. Products obtained from Brand-C showed the highest mislabeling rate (42.85%). The phylogenetic analysis adopted with the TIM2+F+I+G4 model showed the sequenced DNA similar to the NCBI database reference sequence. Overall, mislabeled products of imported seafood were found at the rate of 24.44%, suggesting that strict surveillance for seafood substitution should be implemented in Thailand. Full article

Anaerobic digestion (AD) is a widely applied technology for renewable energy generation using biogas as energy vector. The existing microbial consortium in this technology allows for the use of several types of biomass as substrates. A promising alternative for the production of high-value products (e.g., mixed volatile fatty acids–VFAs, hydrogen) is the use of modified AD. There are several techniques to achieve this objective by modifying the operating conditions of the process. The literature has described the best AD routes for generating renewable energy or high-value products based on specific substrate types and operating conditions. Few studies have reported the integral fraction valorization of the AD process applying the biorefinery concept. This article provides an analysis of the different routes that favor the production of energy carriers and high-value products involving key issues related to operating conditions and substrates. Moreover, AD is addressed through the biorefinery concept. Finally, a case study is presented where renewable energy and mixed VFAs are generated by applying the biorefinery concept in a number of proposed scenarios using organic kitchen food waste (OKFW) as feedstock. The case study involves an experimental and simulation stage. Then, the economic feasibility of the proposed scenarios is evaluated. In conclusion, AD is a promising and economically feasible technology to produce valuable products from several types of waste materials. Full article

A variety of mathematical models have been developed to simulate the biochemical and physico-chemical aspects of the anaerobic digestion (AD) process to treat organic wastes and generate biogas. However, all these models, including the most widely accepted and implemented Anaerobic Digestion Model No.1, remain incapable of adequately representing the material balance of AD and are therefore inherently incapable of material conservation. The absence of robust mass conservation constrains reliable estimates of any kinetic parameters being estimated by regression of empirical data. To address this issue, the present work involved the development of a “framework” for a mass-conserving atomistic mathematical model which is capable of mass conservation, with a relative error in the range of machine precision value and an atom balance with a relative error of ±0.02% whilst obeying the Henry’s law and electroneutrality principle. Implementing the model in an Excel spreadsheet, the study calibrated the model using the empirical data derived from batch studies. Although the model shows high fidelity as assessed via inspection, considering several constraints including the drawbacks of the model and implementation platform, the study also provides a non-exhaustive list of limitations and further scope for development. Full article

Bacterial cellulose is a biocompatible biomaterial with a unique macromolecular structure. Unlike plant-derived cellulose, bacterial cellulose is produced by certain bacteria, resulting in a sustainable material consisting of self-assembled nanostructured fibers with high crystallinity. Due to its purity, bacterial cellulose is appealing for biomedical applications and has raised increasing interest, particularly in the context of 3D printing for tissue engineering and regenerative medicine applications. Bacterial cellulose can serve as an excellent bioink in 3D printing, due to its biocompatibility, biodegradability, and ability to mimic the collagen fibrils from the extracellular matrix (ECM) of connective tissues. Its nanofibrillar structure provides a suitable scaffold for cell attachment, proliferation, and differentiation, crucial for tissue regeneration. Moreover, its mechanical strength and flexibility allow for the precise printing of complex tissue structures. Bacterial cellulose itself has no antimicrobial activity, but due to its ideal structure, it serves as matrix for other bioactive molecules, resulting in a hybrid product with antimicrobial properties, particularly advantageous in the management of chronic wounds healing process. Overall, this unique combination of properties makes bacterial cellulose a promising material for manufacturing hydrogels and 3D-printed scaffolds, advancing the field of tissue engineering and regenerative medicine. Full article

In this research, we explore the potential of employing density functional theory (DFT) for the design of biodegradable hydrogels aimed at capturing carbon dioxide (CO₂) and mitigating greenhouse gas emissions. We employed biodegradable hydrogel models, including polyethylene glycol, polyvinylpyrrolidone, chitosan, and poly-2-hydroxymethacrylate. The complexation process between the hydrogel and CO₂ was thoroughly investigated at the ωB97X-D/6-311G(2d,p) theoretical level. Our findings reveal a strong affinity between the hydrogel models and CO₂, with binding energies ranging from −4.5 to −6.5 kcal/mol, indicative of physisorption processes. The absorption order observed was as follows: chitosan > PVP > HEAC > PEG. Additionally, thermodynamic parameters substantiated this sequence and even suggested that these complexes remain stable up to 160 °C. Consequently, these polymers present a promising avenue for crafting novel materials for CO₂ capture applications. Nonetheless, further research is warranted to optimize the design of these materials and assess their performance across various environmental conditions. Full article

A combination of Poloxamer 407 (P407) and hydroxypropyl methylcellulose (HPMC) hydrosols is proposed as an in situ thermo-gelling vehicle for the nasal drug delivery of chlorhexidine–silver nanoparticles conjugates (SN-CX). Optimization of the formulation was carried out by applying varying ratios of P407 and HPMC in the presence and absence of SN-CX so that gelation would occur in the temperature range of the nasal cavity (30–34 °C). Mechanisms for the observed gelation phenomena were suggested based on viscosimetry, texture analysis, and dynamic light scattering. Tests were carried out for sprayability, washout time, in vitro drug release, ex vivo permeation, and antimicrobial activity. When applied separately, HPMC was found to lower the P407 gelation temperature (T_g), whereas SN-CX increased it. However, in the presence of HPMC, SN-CX interfered with the P407 micellar organization in a principally contrasting way while leading to an even further decrease in T_g. SN-CX-loaded nasal formulations composed of P407 16% and HPMC 0.1% demonstrated a desired gelation at 31.9 °C, good sprayability (52.95% coverage of the anterior nasal cavity), mucoadhesion for 70 min under simulated nasal clearance, expedient release and permeation, and preserved anti-infective activity against seasonal Influenza virus and beta-coronavirus, Pseudomonas aeruginosa, Klebsiella pneumoniae, Staphylococcus aureus and other pathogens. Our findings suggest that the current development could be considered a potential formulation of a protective nasal spray against respiratory infections. Full article

Trichoderma erinaceum is a filamentous fungus that was isolated from decaying sugarcane straw at a Brazilian ethanol biorefinery. This fungus shows potential as a source of plant cell wall-degrading enzymes (PCWDEs). In this study, we conducted a comprehensive multiomics investigation of T. erinaceum to gain insights into its enzymatic capabilities and genetic makeup. Firstly, we performed genome sequencing and assembly, which resulted in the identification of 10,942 genes in the T. erinaceum genome. We then conducted transcriptomics and secretome analyses to map the gene expression patterns and identify the enzymes produced by T. erinaceum in the presence of different substrates such as glucose, microcrystalline cellulose, pretreated sugarcane straw, and pretreated energy cane bagasse. Our analyses revealed that T. erinaceum highly expresses genes directly related to lignocellulose degradation when grown on pretreated energy cane and sugarcane substrates. Furthermore, our secretome analysis identified 35 carbohydrate-active enzymes, primarily PCWDEs. To further explore the enzymatic capabilities of T. erinaceum, we selected a β-glucosidase from the secretome data for recombinant production in a fungal strain. The recombinant enzyme demonstrated superior performance in degrading cellobiose and laminaribiose compared to a well-known enzyme derived from Trichoderma reesei. Overall, this comprehensive study provides valuable insights into both the genetic patterns of T. erinaceum and its potential for lignocellulose degradation and enzyme production. The obtained genomic data can serve as an important resource for future genetic engineering efforts aimed at optimizing enzyme production from this fungus. Full article

Colletotrichum lindemuthianum is a phytopathogenic fungus that causes anthracnose in common beans (Phaseolus vulgaris) and presents a great diversity of pathotypes with different levels of virulence against bean varieties worldwide. The purpose of this study was to establish whether pathotypic diversity is associated with differences in the mycelial growth and secretion of plant-cell-wall-degrading enzymes (PCWDEs). We evaluated growth, hemicellulase and cellulase activity, and PCWDE secretion in four pathotypes of C. lindemuthianum in cultures with glucose, bean hypocotyls and green beans of P. vulgaris, and water hyacinth (Eichhornia crassipes). The results showed differences in the mycelial growth, hemicellulolytic activity, and PCWDE secretion among the pathotypes. Glucose was not the preferred carbon source for the best mycelial growth in all pathotypes, each of which showed a unique PCWDE secretion profile, indicating different levels of carbon catabolite regulation (CCR). The pathotypes showed a high differential hemicellulolytic capacity to degrade host and water hyacinth tissues, suggesting CCR by pentoses and that there are differences in the absorption and metabolism of different monosaccharides and/or disaccharides. We propose that different levels of CCR could optimize growth in different host tissues and could allow for consortium behavior in interactions with bean crops. Full article

An investigation into oomycete diversity in rice paddies of Fars Province in Iran led to the identification of two new Pythium sensu lato (s.l.) species as Globisporangium izadpanahii sp. nov. and Pythium banihashemianum sp. nov. The identification was based on morphological and physiological features as well as on the phylogenetic analysis of nuclear (ITS and βtub) and mitochondrial (cox1 and cox2) loci using Bayesian inference and Maximum Likelihood. The present paper formally describes these two new species and defines their phylogenetic relationships with other congeneric species. According to multiple gene genealogy analysis, G. izadpanahii sp. nov. was grouped with other species of Globisporangium (formerly, clade G of Pythium s.l.) and was closely related to both G. nagaii and the recently described G. coniferarum. The second species, designated P. banihashemianum sp. nov., was grouped with other species of Pythium sensu stricto (formerly, clade B of Pythium s.l.) and, according to the phylogenetic analysis, shared an ancestor with P. plurisporium. The production of globose hyphal swellings was a major characteristic of G. izadpanahii sp. nov., which did not produce vesicles and zoospores. In pathogenicity tests on rice seedlings, P. banihashemianum sp. nov. isolates were highly pathogenic and caused severe root and crown rot, while G. izadpanahii sp. nov. isolates were not pathogenic. Full article

This paper addresses a void in the research on auditing and initial public offering (IPO) failure by investigating the impact of the Big 4 auditing firms on the likelihood of an IPO failure. This research is the first comprehensive analysis of more than 33,000 global IPOs that either failed or were successful between 1995 and 2019 across a wide range of nations with vastly different regulatory, cultural, and economic settings. A cross-sectional probit regression model is utilized to investigate the influence of hiring the Big 4 auditing firms on IPO failure, building upon prior studies on IPO failure. We found strong evidence that IPO failure rates were diminished by up to 67% when one of the Big 4 auditing firms was involved in auditing the IPO prospectus. For IPO founders, hiring Big 4 auditors before an IPO is a quality signaling strategy that minimizes the risk of a failed IPO by reducing information asymmetry among IPO participants. Our findings provide useful policy implications. Hiring one of the Big 4 auditing firms before an IPO is a reassuring signaling strategy for founders, since it decreases information asymmetry among IPO investors and so lowers the risk of the IPO failing. Primary market investors now have access to credible evidence indicating that backing IPOs from companies that use the Big 4 auditing firms increases the likelihood of such IPOs being listed on stock exchanges and yields positive returns. This is the first time, as far as the academicians are aware, that conclusive evidence has been found of a strong inverse association between the presence of Big 4 audits and failure risk for IPO firms. Our research could be helpful to primary market regulators since it shows how crucial it is to encourage Big 4 audits in IPO companies. The quality work of the Big 4 auditors does lower the risk of failure in the IPO market, which might help owners of small private equities to list their firms on the IPO market, boosting economic growth. Full article

In recent years, the prevalence of and mortality associated with cardiovascular diseases have been rising in most countries and regions. AF is the most common arrhythmic condition, and there are several treatment options for AF. Pulmonary vein isolation is an effective treatment for AF and is the cornerstone of current ablation techniques, which have one major limitation: even when diagnosed and treated at a facility that specializes in ablation, patients have a greater chance of recurrence. Therefore, there is a need to develop better ablation techniques for the treatment of AF. This article first compares the current cryoablation (CBA) and radiofrequency ablation (RFA) techniques for the treatment of AF and discusses the utility and advantages of the development of pulsed-field ablation (PFA) technology. The current research on PFA is summarized from three perspectives, namely, simulation experiments, animal experiments, and clinical studies. The results of different stages of experiments are summarized, especially during animal studies, where pulmonary vein isolation was carried out effectively without causing injury to the phrenic nerve, esophagus, and pulmonary veins, with higher safety and shorter incision times. This paper focuses on a review of various a priori and clinical studies of this new technique for the treatment of AF. Full article

This study investigates the mechanical behavior of femur bones under loading conditions, focusing on the transition from elastic to plastic deformation and eventual fracture. The force–displacement curves reveal distinct phases of deformation, with an initial linear relationship indicating elastic behavior, followed by deviation from linearity marking the onset of plastic deformation. Fracture occurs beyond a critical load, leading to a sharp drop in the force–displacement curve. The maximum fracture force varies among specimens and is influenced by bone geometry, size, cross-sectional area, and cortical thickness. Post-failure analysis highlights additional insights into fracture mechanics and bone material toughness. Reinforcing bones with screws enhances their strength, which is evident in the higher fracture forces observed in force–displacement diagrams. Fixation procedures following fractures further increase bone strength. Comparing specimens with and without strengthening underscores the effectiveness of reinforcement methods in improving bone mechanical properties. After analyzing the results, it is evident that femur bones with reinforcement can withstand greater loads, and they can also absorb higher impact energies while remaining in the elastic deformation range and without suffering permanent plastic damage. This study provides valuable insights into bone biomechanics and the efficacy of reinforcement techniques in enhancing bone strength and fracture resistance. Full article

Ameloblastoma (AM), periapical cyst (PC), and chronic suppurative osteomyelitis (CSO) are prevalent maxillofacial diseases with similar imaging characteristics but different treatments, thus making preoperative differential diagnosis crucial. Existing deep learning methods for diagnosis often require manual delineation in tagging the regions of interest (ROIs), which triggers some challenges in practical application. We propose a new model of Wavelet Extraction and Fusion Module with Vision Transformer (WaveletFusion-ViT) for automatic diagnosis using CBCT panoramic images. In this study, 539 samples containing healthy (n = 154), AM (n = 181), PC (n = 102), and CSO (n = 102) were acquired by CBCT for classification, with an additional 2000 healthy samples for pre-training the domain-adaptive network (DAN). The WaveletFusion-ViT model was initialized with pre-trained weights obtained from the DAN and further trained using semi-supervised learning (SSL) methods. After five-fold cross-validation, the model achieved average sensitivity, specificity, accuracy, and AUC scores of 79.60%, 94.48%, 91.47%, and 0.942, respectively. Remarkably, our method achieved 91.47% accuracy using less than 20% labeled samples, surpassing the fully supervised approach’s accuracy of 89.05%. Despite these promising results, this study’s limitations include a low number of CSO cases and a relatively lower accuracy for this condition, which should be addressed in future research. This research is regarded as an innovative approach as it deviates from the fully supervised learning paradigm typically employed in previous studies. The WaveletFusion-ViT model effectively combines SSL methods to effectively diagnose three types of CBCT panoramic images using only a small portion of labeled data. Full article

In recent years, contaminants of emerging concern have been reported in several environmental matrices due to advances in analytical methodologies. These anthropogenic micropollutants are detected at residual levels, representing an ecotoxicological threat to aquatic ecosystems. In particular, the pharmacotherapeutic group of non-steroidal anti-inflammatories (NSAIDs) is one of the most prescribed and used, as well as one of the most frequently detected in the aquatic environment. Bivalves have several benefits as a foodstuff, and also as an environment bioindicator species. Therefore, they are regarded as an ideal tool to assess this issue from both ecotoxicological and food safety perspectives. Thus, the control of these residues in bivalves is extremely important to safeguard environmental health, also ensuring food safety and public health. This paper aims to review NSAIDs in bivalves, observing their consumption, physicochemical characteristics, and mechanisms of action; their environmental occurrence in the aquatic environment and aquatic biota; and their effects on the ecosystem and the existent legal framework. A review of the analytical methodologies for the determination of NSAIDs in bivalves is also presented. Full article

Cassava tuber is an essential staple crop in tropical regions with versatile applications in the food, feed, and industrial sectors. However, its high moisture content and perishable nature necessitate efficient preservation methods to extend its shelf life and enhance its value. Pre-drying, drying, and post-drying processes play pivotal roles in maintaining the quality and usability of cassava products. This review comprehensively examines the current status and future directions in the pre-drying, drying, and post-drying processes of cassava tuber. Various pre-drying or pretreatment methods and drying techniques are evaluated for their impacts on drying kinetics and product quality. Additionally, challenges and limitations in achieving high-quality processing of cassava flour are identified. Future directions in cassava drying methods emphasize the integration of combined pre-drying and drying techniques to optimize resource utilization and processing efficiency. Furthermore, the adoption of advanced online measurement and control technologies in drying equipment is highlighted for real-time monitoring and optimization of drying parameters. The importance of optimizing existing processes to establish a comprehensive cassava industrial chain and foster the development of the cassava deep-processing industry is emphasized. This review provides valuable insights into the current trends and future prospects in cassava drying technologies, aiming to facilitate sustainable and efficient utilization of cassava resources for various applications. Full article

In this study, a novel strain for degrading chitin was identified as Bacillus paralicheniformis HL37, and the key chitinase CH1 was firstly mined through recombinant expression in Bacillus amyloliquefaciens HZ12. Subsequently, the sequence composition and catalytic mechanism of CH1 protein were analyzed. The molecular docking indicated that the triplet of Asp526, Asp528, and Glu530 was a catalytic active center. The enzymatic properties analysis revealed that the optimal reaction temperature and pH was 65 °C and 6.0, respectively. Especially, the chitinase activity showed no significant change below 55 °C and it could maintain over 60% activity after exposure to 85 °C for 30 min. Moreover, the optimal host strain and signal peptide were obtained to enhance the expression of chitinase CH1 significantly. As far as we know, it was the first time finding the highly efficient chitin-degrading enzymes in B. paralicheniformis, and detailed explanations were provided on the catalytic mechanism and enzymatic properties on CH1. Full article

Nitrogen fertilization level and harvesting season significantly impact tea aroma quality. In this study, we analyzed the volatile organic compounds of fresh Jin Xuan (JX) tea leaves under different nitrogen application levels (N0, N150, N300, N450) during summer and autumn. A total of 49 volatile components were identified by gas chromatography–mass spectrometry (GC-MS). Notably, (E)-2-hexenal, linalool, and geraniol were the main contributors to the aroma of fresh JX leaves. The no-nitrogen treatment (N0) presented the greatest quantity and variety of volatiles in both seasons. A greater difference in volatile compounds was observed between nitrogen treatments in summer vs. autumn. The N0 treatment had a greater total volatile concentration in summer, while the opposite was observed in the nitrogen application treatments (N150, N300, N450). Summer treatments appeared best suited to black tea production. The concentration of herbaceous aroma-type volatiles was higher in summer, while the concentration of floral volatiles was higher in autumn. Volatile concentrations were highest in the N0 and N450 treatments in autumn and appeared suitable for making black tea and oolong tea. Overall, this research provides valuable insights into how variations in N application rates across different harvesting seasons impact the aroma characteristics of tea leaves. Full article